Computing technology and the Internet have fundamentally changed the processes of disseminating and consuming information. Hundreds of millions of people around the world have access to multiple means of accessing and consuming a vast array of information at virtually any time. For instance, users can listen to songs using a portable music player, watch television shows or videos using mobile devices, or read newspaper articles from around the globe via an electronic reader. In addition, newer and more efficient means of consuming and disseminating information are constantly being generated.
The sheer quantity of information options available to users can make selecting information for consumption challenging. Popular online services can receive tens of hours worth of newly uploaded user-generated content every minute. A technique that has been commonly employed to assist users in identifying information for consumption includes ranking information based on quantities of consumption or user rankings. This allows users to select content that has been consumed by a large number of users in the past, or that a large number of users have previously ranked highly.
However, based on the large quantity of information available, reliance on the ratings and viewership of past users can still result in a relatively large number of information options. In addition, identifying information for consumption based on other users' past opinions may only be marginally helpful with regard to assisting a current user in selecting information for consumption, and can fail to capitalize on quickly changing information consumption trends.